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Colloidal nanoparticles in liquid crystals : bulk properties, biaxiality and untwisting in cholesterics
No full textWe study the effects of colloidal nanoparticles (NPs) in liquid crystal samples in the dilute limit, in a Landau–de Gennes theoretical framework. The effects of the suspended NPs are captured by a homogenized energy, as outlined in Canevari & Zarnescu (2020a, Design of effective bulk potentials for nematic liquid crystals via colloidal homogenisation. Math. Models Methods Appl. Sci., 30:309–342). For spatially homogeneous samples, we explicitly compute the critical points and minimizers of the modified Landau–de Gennes energy and show that the presence of NP eliminates the first-order isotropic-nematic phase transition, stabilizes elusive biaxial phases over some temperature ranges and that the symmetry of the NP boundary conditions or surface treatments dictates the bulk equilibrium phase at high temperatures. We also numerically demonstrate structural transitions from twisted helical director profiles to untwisted director profiles in cholesteric-filled channel geometries, driven by the collective effects of the NPs and increasing temperature. These transitions are reversible upon lowering the temperature in sufficiently large domains, where thermal hysteresis can also be observed. This behaviour opens interesting avenues for tuning the optical properties of confined, nano-doped cholesteric systems
Emergence of PHMB resistance in Acanthamoeba castellanii and observations on cross-resistance to other frontline therapeutics
No full textPurpose: Acanthamoeba keratitis (AK) is a sight threatening infection of the cornea caused by opportunistic pathogens belonging to the genus Acanthamoeba. AK is commonly associated with contact lens use, and treatments are currently limited and ineffective. As such, the emergence of antimicrobial resistance in Acanthamoeba poses a significant challenge to the management of AK. This study investigates the development of polyhexamethylene biguanide (PHMB) resistance, a frontline therapeutic, in Acanthamoeba trophozoites and explores potential cross-resistance to hexamidine and voriconazole. Methods: Acanthamoeba castellanii trophozoites were exposed and maintained in PHMB starting at 2 µg/mL and increasing upon reaching confluence. Cells were subsequently exposed to incrementally higher doses of PHMB in a stepwise manner (2, 4, 5.5, and 7 µg/mL). When sustained growth under PHMB exposure was observed, morphology was assessed by imaging flow cytometry and susceptibility assays were performed by incubating resistant strains with PHMB, hexamidine, and voriconazole for 24 hours, and viability determined using alamarBlue. Results: Trophozoites surviving exposure at 2 µg/mL reached confluence within 11 days. Stepwise increases to 4 µg/mL, 5.5 µg/mL, and 7 µg/mL were achieved within 6 – 7 days at each stage. PHMB-resistant Acanthamoeba strains exhibited a 9-fold increase in resistance to PHMB relative to naïve cells, alongside significant cross-resistance to voriconazole (159-fold) and hexamidine (8.4-fold). No significant change in trophozoite or cyst morphology was observed relative to the naïve cell line. Conclusions: These findings represent the first known laboratory-induced PHMB-resistant Acanthamoeba strains, raising concerns regarding the longevity of current therapeutic options and the potential for cross-resistance to alternative treatments. This highlights the need for clinical vigilance and further investigation into the molecular mechanisms of resistance to better inform treatment strategies
Pregnancy, baby, and childhood outcomes from using anti-seizure medication during pregnancy
No full textBackground: Evidence of the safety of some anti-seizure medicines (ASMs) during pregnancy remains uncertain. Methods: We conducted a population-based cohort study of singleton pregnancies in Scotland conceived between 01/04/2010-02/07/2023. Exposure was ‘Any ASM’ prescription issued 28 days prior to conception up to pregnancy end. Seven monotherapies were also examined: valproate, topiramate, carbamazepine, lamotrigine, levetiracetam, gabapentin, and pregabalin. Unexposed comparators were matched to the exposed on gestational age and year of conception. Pregnancy loss, congenital condition, and child development outcomes were compared by exposure status using conditional logistic regression to account for the matched study design. Results: Here we show pregnancy loss (3,175/11,011 pregnancies, 28.8% vs. 24,040/107,889 pregnancies, 22.3%), congenital conditions (230/8,370 babies, 2.7% vs. 1,693/82,085 babies, 2.1%) and developmental concerns (1,270/4,890 live births, 26.0% vs. 7,658/48,883 live births, 15.7%) are more common following any ASM exposure in pregnancy compared with no ASM exposure in pregnancy. Valproate is strongly associated with pregnancy loss (adjusted odds ratio (aOR): 1.92, 95% confidence interval (CI): 1.50-2.47), congenital conditions (aOR: 1.85, 95% CI: 1.06-3.21) and developmental concerns (aOR: 1.43, 95% CI: 1.01-2.03). Pregabalin, gabapentin, and Any ASM are also associated with pregnancy loss and developmental concerns. Conclusions: Our findings corroborate the associated risks of valproate use and embryo malformations, support the use of lamotrigine and levetiracetam in pregnancy, and raise concerns regarding gabapentinoid use in pregnancy
Towards a harmonized database of indoor air contaminant concentrations : methods and application to CO2
Full text linkReliable data on indoor air quality (IAQ) is essential for design and operation of energy-efficient and healthy residential buildings. This work introduces a standardized methodology to systematically compile, process, and analyze IAQ measurement data, enabling the creation of a harmonized IAQ database. Developed within the IEA EBC Annex 86 project, the open-source algorithm ensures compatibility, data protection, and scalability by aggregating time series measurements into monthly summaries while preserving distributional information and linking them to relevant meta-information. This methodology was applied to create a comprehensive, open-access dataset integrating indoor air contaminant measurements from numerous IAQ studies covering residential homes with varied building types, ventilation strategies, climates and occupancy patterns. The dataset enables consistent statistical analysis and interpretation of IAQ data collected through heterogeneous methods, supporting robust cross-study comparison and benchmarking. As the dataset grows, it will enable harm-based analyses, an emerging paradigm in indoor air quality regulation. The paper exemplifies the utility of the harmonized database by analyzing CO2 concentration data from 18 studies encompassing over 1,000 homes, with a focus on bedrooms. The results quantify the concentration distribution encountered in bedrooms across diverse regulatory and building contexts. Results highlight significant variations in CO2 levels influenced by ventilation type, building characteristics, and ambient temperature, emphasizing the importance of standardized data for advancing IAQ research, policy development, and occupant health assessments. Ultimately, the harmonized dataset and methodology can serve as a critical resource for researchers, practitioners, and regulators aiming to optimize indoor environmental quality while advancing energy-efficient residential building design globally
Exploring the barriers and opportunities for a more predictive data-driven telecare service : qualitative study in Scotland
Full text linkBackground: Telecare uses technology to help people live more independently at home. When an adverse event (such as someone falling or a bath overflowing) happens, the technology reactively senses this and alerts a call center to respond. If the technology can detect a person’s current (and past) states and behaviors, with machine learning, we can more proactively identify potential risks before an adverse event occurs and intervene. Despite social care organizations being data-rich, few predictive analytics are currently routinely applied. There is a need to understand current data management practices before optimizing organizational and technical readiness for proactive data-driven telecare services. Objective: The aim of this study was to understand how specific telecare data (monitoring falls, identifying people at risk of falling, and providing services in response) are collected, managed, and used in the largest health and social care region in Scotland. The objectives were to: (1) map the community alarm data flow to understand what data were being collected, by what services, and where it was stored, linked, and managed; and (2) identify the current barriers and opportunities around staff and organizations using or applying predictive analytics routinely within telecare service provision. Methods: This qualitative study involved interviews with health and social care professionals working in Glasgow City Council (GCC) and a telecare service provider (Tunstall). Interviews explored experiences of the systems and data access, processes for collecting and using the data, and how it might be better used to target services more proactively. Data underwent a thematic framework analysis. Descriptions of the data flow were used to develop visual representations of the sociotechnical system. Results: A total of 14 participants at operational and managerial levels took part. A complex sociotechnical telecare system was identified, involving multiple staff roles, with data exchanged across 11 teams, using 17 systems, with 4 distinct data sources. In total, four themes highlighted key challenges that are: (1) suboptimal systems and equipment; (2) data recording inefficiencies and use; (3) specific patient population barriers and IT literacy; and (4) limited resources and support. Opportunities for more predictive telecare included: establishing a more structured and integrated approach to data management; scope for improved data organization and retrieval; better cross-platform integration and data sharing; and the use of tools or models to support insightful data analysis tailored to the users. Conclusions: Scottish telecare data services require improved infrastructure to be managed in ways that support more predictive telecare services. This includes more structured and linked datasets and greater integration between the services and systems to allow service providers more integrated, up-to-date, and real-time connected data to build accurate and meaningful models
Bone-mimicking TPMS gyroid design for osteoporotic fracture fixation : analysis of structural efficiency with simulation study on 316L stainless steel
Full text linkFracture fixation devices for load-bearing bones are typically made from stainless steel, titanium, or cobalt-chromium alloys, but their high stiffness can cause stress shielding with detrimental effect on bone density, reducing implant anchorage, leading to implant loosening, particularly in osteoporotic bones. Low-elastic materials reduce stress shielding, but issues with implant anchorage in fixation of brittle bone cause persistent implant failures. Effective management of porous designs that promote bone ingrowth while maintaining structural efficiency is crucial in these cases. This study addresses the issue by proposing gyroid-based lattice designs that mimic cortical bone porosity (5–15%) for fracture fixation plates, aiming to reduce stress shielding and enhance bone ingrowth for improved anchorage. A three-stage Implicit Finite Element Analysis (FEA) was performed using Ansys. Static four-point bending simulations confirmed the design's suitability for 316L stainless steel in femur, tibia, and humerus fixation in accordance with United States Food and Drug Administration (US-FDA) criteria. Tensile simulations showed a reduction in Young’s modulus from 193 to 178 GPa, indicating an 8% reduction in elasticity. This shift moves closer to the elasticity of bone, representing progress in minimizing stress shielding effect while balancing strength. Biomechanical simulation with plate and bone interaction also demonstrated six-time increased stress flow to the bone with porous gyroid structured implant. In comparison, simple cubic lattice designs failed to meet US-FDA criteria, while the gyroid designs exhibited 25% higher mechanical properties. The gyroid design shows promise in improving bone stability, reducing stress shielding, and meeting US-FDA performance criteria in load-bearing fractures. Graphical abstract
Accelerated drug development using a digital formulator and a self-driving tableting data factory
No full textPharmaceutical tablet formulation and process development, traditionally a complex and multi-dimensional decision-making process, necessitates extensive experimentation and resources, often resulting in suboptimal solutions. This study presents an integrated platform for tablet formulation and manufacturing, built around a Digital Formulator and a Self-Driving Tableting DataFactory. By combining predictive modelling, optimisation algorithms, and automation, this system offers a material-to-product approach to predict and optimise critical quality attributes for different formulations, linking raw material attributes to key blend and tablet properties, such as flowability, porosity, and tensile strength. The platform leverages the Digital Formulator, an in-silico optimisation framework that employs a hybrid system of models – melding data-driven and mechanistic models – to identify optimal formulation settings for manufacturability. Optimised formulations then proceed through the self-driving Tableting DataFactory, which includes automated powder dosing, tablet compression and performance testing, followed by iterative refinement of process parameters through Bayesian optimisation methods. This approach accelerates the timeline from material characterisation to development of an in-specification tablet within 6 hours, utilising less than 5 grams of API, and manufacturing small batch sizes of up to 1,440 tablets with augmented and mixed reality enabled real-time quality control within 24 hours. Validation across multiple APIs and drug loadings underscores the platform’s capacity to reliably meet target quality attributes, positioning it as a transformative solution for accelerated and resource-efficient pharmaceutical development
Early childhood and early adolescent predictors of internalising symptoms in adolescents : findings from a longitudinal study in a high-risk South African environment
Full text linkPurpose This study investigates predictors of internalising symptoms among adolescents aged 16 to 19 years in a high-risk context in South Africa. Specifically, it explores early childhood (antenatal to 18 months postpartum), and early adolescent (13 to 14 years) predictors of internalising symptoms measured during later adolescence (16–19 years), aiming to identify key factors influencing mental health outcomes in this vulnerable population. Methods Utilising a unique 18-year longitudinal dataset, we included a total of 314 adolescent participants from South Africa in the analysis and employed an adaptive elastic net regularised regression to analyse the effects of 18 predictors from early childhood and early adolescence on internalising symptoms at ages 16 to 19 years. The broadband scale for “internalising” from the Youth Self Report (ages 11–18) was used as the outcome measure. Data collected at five time points across three phases of the longitudinal study were included in the analysis. Results Key predictors of internalising symptoms were female sex (β=-4.30; 95% CI [-4.42;4.19]). Early childhood predictors with significant associations were maternal depression (β = 1.70; 95% CI [1.56;1.84]) and caregiver employment (β=-0.37; 95% CI [-0.46;-0.29]). In early adolescence, significant predictors included informal house type (β = 0.82; 95% CI [0.71;0.93]), caregiver alcohol use (β = 0.74; 95% CI [0.67;0.81]), exposure to violence (β = 0.73; 95% CI [0.67;0.78]), friend support (β=-0.61; 95% CI [-0.67;-0.55]), food insecurity (β = 0.51; 95% CI [0.46;0.56]), family support (β=-0.33; 95% CI [-0.37;-0.29]), and self-esteem (β=-0.33; 95% CI [-0.37;-0.29]). Conclusion This study identifies key predictors of internalising symptoms in adolescents from high-risk context, focusing on caregiver variables and social connections. Maternal / Primary cargiver depression and caregiver unemployment in early childhood have lasting effects, highlighting the need for early intervention. In early adolescence, factors such as social environment and caregiver stability are crucial. These insights can inform targeted interventions and policies to support adolescent mental health in high-risk contexts
On a run-based δ-shock model with two critical levels
Full text linkIn reliability engineering, the δ-shock model is used to study shock-exposed systems that are sensitive to the length of the time distance between consecutive shocks. When the system failure depends on a certain number of consecutive shocks with an inter-arrival time within a critical range, we are dealing with a run-based δ-shock model. In this paper, a new run-based δ-shock model is introduced, under which the system fails when an inter-arrival time is less than a critical threshold δ1 for the first time or k consecutive inter-arrival times fall in the interval (δ1, δ2), for 0 ≤ δ1, < δ2. We study the probability behavior of the system’s stopping time as well as the survival of the system under the proposed model. As an illustrative example, we examine the survival of the system when the arrival of shocks follows a Poisson process. Furthermore, an example of applications is provided to illustrate possible application aspects
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